Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x1bd16fce048>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x1bd170d5a90>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.11.0
Default GPU Device: /device:GPU:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    input_images = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name="input_images")
    input_z = tf.placeholder(tf.float32, (None, z_dim), name="input_z")
    lr = tf.placeholder(tf.float32, (None), name="learning_rate")
    
    return input_images, input_z, lr


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    alpha_relu = 0.2
    with tf.variable_scope("discriminator", reuse=reuse):
        #input layer 28x28x3
        x1 = tf.layers.conv2d(images, 64, 4, strides=2, padding='same',
                             kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d())
        relu1 = tf.maximum(alpha_relu * x1, x1)
        # output 14x14x64
        
        x2 = tf.layers.conv2d(relu1, 128, 4, strides=2, padding='same',
                             kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d())
        relu2 = tf.maximum(alpha_relu * x2, x2)
        #output 7x7x128
        
        x3 = tf.layers.conv2d(relu2, 256, 4, strides=2, padding='same',
                             kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d())
        relu3 = tf.maximum(alpha_relu * x3, x3)
        #output 4x4x256
        
        x4 = tf.layers.conv2d(relu3, 512, 4, strides=1, padding='same',
                             kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d())
        relu4 = tf.maximum(alpha_relu * x4, x4)
        #output 4x4x512
        
        flattten = tf.reshape(relu4, (-1, 4*4*512))
        logits = tf.layers.dense(flattten, 1, kernel_initializer= tf.contrib.layers.xavier_initializer())
        output = tf.sigmoid(logits)

    return output, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [8]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    alpha_relu = 0.2
    drop_value = 0.5
    with tf.variable_scope("generator", reuse= not is_train):
        # fully connected layer 
        x1 = tf.layers.dense(z, 4*4*512, kernel_initializer=tf.contrib.layers.xavier_initializer())
        # convolution stack
        x1 = tf.reshape(x1, (-1, 4, 4, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.layers.dropout(x1, rate=drop_value)
        x1 = tf.maximum(alpha_relu * x1, x1)
        # output 4x4x512
        
        x2 = tf.layers.conv2d_transpose(x1, 256, 4, strides=1, padding='valid', 
                                        kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d())
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.layers.dropout(x2, rate=drop_value)
        x2 = tf.maximum(alpha_relu * x2, x2)
        # output 7x7x256
        
        x3 = tf.layers.conv2d_transpose(x2, 128, 4, strides=2, padding='same',
                                       kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d())
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.layers.dropout(x3, rate=drop_value)
        x3 = tf.maximum(alpha_relu * x3, x3)
        # output 14x14x128
        
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 4, strides=2, padding='same',
                                           kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d())
        #output 28x28xout_channel_dim
        
        output = tf.tanh(logits)
        
    return output


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [9]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim)
    smooth = 0.1
    
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, 
                                                labels=tf.ones_like(d_model_real) * (1 - smooth))
    )
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, 
                                                labels=tf.zeros_like(d_model_fake))
    )
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
                                                labels=tf.ones_like(d_model_fake) * (1 - smooth))
    )
    
    d_loss = d_loss_real + d_loss_fake
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [10]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    
    # Optimizer
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
        
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [11]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [12]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    inputs_real, inputs_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(inputs_real, inputs_z, data_shape[-1])
    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    steps = 0
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps += 1
                batch_images = batch_images * 2

                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                # Run optimizers
                _ = sess.run(d_train_opt, feed_dict={inputs_real: batch_images, inputs_z: batch_z, lr:learning_rate})
                _ = sess.run(g_train_opt, feed_dict={inputs_z: batch_z, lr:learning_rate})

                if steps % 100 == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({inputs_z:batch_z, inputs_real: batch_images})
                    train_loss_g = g_loss.eval({inputs_z:batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                if steps % 200 == 0:
                    show_generator_output(sess, 25, inputs_z, data_shape[3], data_image_mode)

                   

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [16]:
batch_size = 16
z_dim = 128
learning_rate = 0.0003
beta1 = 0.1


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 1.4340... Generator Loss: 0.6193
Epoch 1/2... Discriminator Loss: 1.3724... Generator Loss: 0.8620
Epoch 1/2... Discriminator Loss: 1.4279... Generator Loss: 0.5711
Epoch 1/2... Discriminator Loss: 1.5261... Generator Loss: 0.6096
Epoch 1/2... Discriminator Loss: 1.3238... Generator Loss: 0.6625
Epoch 1/2... Discriminator Loss: 1.4721... Generator Loss: 0.6440
Epoch 1/2... Discriminator Loss: 1.3815... Generator Loss: 0.6192
Epoch 1/2... Discriminator Loss: 1.1457... Generator Loss: 0.8917
Epoch 1/2... Discriminator Loss: 1.1833... Generator Loss: 0.8348
Epoch 1/2... Discriminator Loss: 1.2321... Generator Loss: 0.7858
Epoch 1/2... Discriminator Loss: 1.2227... Generator Loss: 0.6707
Epoch 1/2... Discriminator Loss: 1.4267... Generator Loss: 0.6293
Epoch 1/2... Discriminator Loss: 1.1250... Generator Loss: 0.8659
Epoch 1/2... Discriminator Loss: 0.9661... Generator Loss: 1.0685
Epoch 1/2... Discriminator Loss: 1.1374... Generator Loss: 0.7979
Epoch 1/2... Discriminator Loss: 1.0706... Generator Loss: 0.9471
Epoch 1/2... Discriminator Loss: 1.1821... Generator Loss: 0.7933
Epoch 1/2... Discriminator Loss: 1.4956... Generator Loss: 0.5128
Epoch 1/2... Discriminator Loss: 1.2564... Generator Loss: 0.6950
Epoch 1/2... Discriminator Loss: 1.1163... Generator Loss: 0.7980
Epoch 1/2... Discriminator Loss: 0.8271... Generator Loss: 1.1956
Epoch 1/2... Discriminator Loss: 0.9424... Generator Loss: 0.9697
Epoch 1/2... Discriminator Loss: 1.2877... Generator Loss: 0.6979
Epoch 1/2... Discriminator Loss: 0.7536... Generator Loss: 1.5774
Epoch 1/2... Discriminator Loss: 0.9292... Generator Loss: 1.2656
Epoch 1/2... Discriminator Loss: 1.0309... Generator Loss: 0.8549
Epoch 1/2... Discriminator Loss: 0.8791... Generator Loss: 1.2323
Epoch 1/2... Discriminator Loss: 1.1421... Generator Loss: 0.8078
Epoch 1/2... Discriminator Loss: 0.8657... Generator Loss: 1.1862
Epoch 1/2... Discriminator Loss: 1.1749... Generator Loss: 0.7350
Epoch 1/2... Discriminator Loss: 1.4856... Generator Loss: 0.5446
Epoch 1/2... Discriminator Loss: 1.1489... Generator Loss: 0.7239
Epoch 1/2... Discriminator Loss: 0.8006... Generator Loss: 1.1940
Epoch 1/2... Discriminator Loss: 0.8347... Generator Loss: 1.0756
Epoch 1/2... Discriminator Loss: 1.0707... Generator Loss: 0.8010
Epoch 1/2... Discriminator Loss: 0.8106... Generator Loss: 1.1350
Epoch 1/2... Discriminator Loss: 1.0529... Generator Loss: 1.1804
Epoch 2/2... Discriminator Loss: 1.1583... Generator Loss: 0.7199
Epoch 2/2... Discriminator Loss: 0.8576... Generator Loss: 1.1828
Epoch 2/2... Discriminator Loss: 1.5699... Generator Loss: 0.5185
Epoch 2/2... Discriminator Loss: 0.6987... Generator Loss: 1.3838
Epoch 2/2... Discriminator Loss: 0.8278... Generator Loss: 1.3399
Epoch 2/2... Discriminator Loss: 0.9562... Generator Loss: 1.0353
Epoch 2/2... Discriminator Loss: 0.8311... Generator Loss: 1.0649
Epoch 2/2... Discriminator Loss: 0.8395... Generator Loss: 1.2837
Epoch 2/2... Discriminator Loss: 0.9889... Generator Loss: 1.0445
Epoch 2/2... Discriminator Loss: 0.8727... Generator Loss: 1.2448
Epoch 2/2... Discriminator Loss: 0.8366... Generator Loss: 1.0767
Epoch 2/2... Discriminator Loss: 1.0404... Generator Loss: 0.8062
Epoch 2/2... Discriminator Loss: 0.9917... Generator Loss: 1.1573
Epoch 2/2... Discriminator Loss: 0.5998... Generator Loss: 1.7293
Epoch 2/2... Discriminator Loss: 0.7789... Generator Loss: 1.0613
Epoch 2/2... Discriminator Loss: 1.0770... Generator Loss: 0.8277
Epoch 2/2... Discriminator Loss: 0.8250... Generator Loss: 1.5949
Epoch 2/2... Discriminator Loss: 0.7516... Generator Loss: 1.4469
Epoch 2/2... Discriminator Loss: 0.7483... Generator Loss: 1.3992
Epoch 2/2... Discriminator Loss: 0.7133... Generator Loss: 1.8712
Epoch 2/2... Discriminator Loss: 0.9152... Generator Loss: 1.6916
Epoch 2/2... Discriminator Loss: 0.6837... Generator Loss: 1.4834
Epoch 2/2... Discriminator Loss: 0.8449... Generator Loss: 1.1333
Epoch 2/2... Discriminator Loss: 0.6611... Generator Loss: 1.5143
Epoch 2/2... Discriminator Loss: 0.7944... Generator Loss: 1.5019
Epoch 2/2... Discriminator Loss: 0.8336... Generator Loss: 1.2630
Epoch 2/2... Discriminator Loss: 0.7925... Generator Loss: 1.3633
Epoch 2/2... Discriminator Loss: 0.8519... Generator Loss: 1.1504
Epoch 2/2... Discriminator Loss: 1.2025... Generator Loss: 0.9105
Epoch 2/2... Discriminator Loss: 0.9611... Generator Loss: 1.0253
Epoch 2/2... Discriminator Loss: 0.7469... Generator Loss: 1.4061
Epoch 2/2... Discriminator Loss: 1.1328... Generator Loss: 0.8045
Epoch 2/2... Discriminator Loss: 0.5710... Generator Loss: 2.1346
Epoch 2/2... Discriminator Loss: 0.7012... Generator Loss: 1.4025
Epoch 2/2... Discriminator Loss: 0.6936... Generator Loss: 1.5604
Epoch 2/2... Discriminator Loss: 0.6218... Generator Loss: 1.5424
Epoch 2/2... Discriminator Loss: 0.8464... Generator Loss: 1.1966
Epoch 2/2... Discriminator Loss: 0.7949... Generator Loss: 1.5259

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [17]:
batch_size = 16
z_dim = 128
learning_rate = 0.0002
beta1 = 0.3 #0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 1.2114... Generator Loss: 0.8301
Epoch 1/1... Discriminator Loss: 0.9608... Generator Loss: 1.1243
Epoch 1/1... Discriminator Loss: 1.0367... Generator Loss: 1.0600
Epoch 1/1... Discriminator Loss: 1.1196... Generator Loss: 0.9316
Epoch 1/1... Discriminator Loss: 1.0244... Generator Loss: 0.9778
Epoch 1/1... Discriminator Loss: 1.2280... Generator Loss: 0.8483
Epoch 1/1... Discriminator Loss: 1.2975... Generator Loss: 0.7632
Epoch 1/1... Discriminator Loss: 1.3734... Generator Loss: 0.7268
Epoch 1/1... Discriminator Loss: 1.2661... Generator Loss: 0.8159
Epoch 1/1... Discriminator Loss: 1.3147... Generator Loss: 0.8112
Epoch 1/1... Discriminator Loss: 1.3154... Generator Loss: 0.8526
Epoch 1/1... Discriminator Loss: 1.2422... Generator Loss: 0.7928
Epoch 1/1... Discriminator Loss: 1.3179... Generator Loss: 0.8148
Epoch 1/1... Discriminator Loss: 1.1619... Generator Loss: 0.8267
Epoch 1/1... Discriminator Loss: 1.4046... Generator Loss: 0.7212
Epoch 1/1... Discriminator Loss: 1.3657... Generator Loss: 0.6682
Epoch 1/1... Discriminator Loss: 1.0976... Generator Loss: 0.9757
Epoch 1/1... Discriminator Loss: 1.2333... Generator Loss: 0.7941
Epoch 1/1... Discriminator Loss: 1.3035... Generator Loss: 0.7199
Epoch 1/1... Discriminator Loss: 1.2734... Generator Loss: 0.8534
Epoch 1/1... Discriminator Loss: 1.2333... Generator Loss: 0.8225
Epoch 1/1... Discriminator Loss: 1.3543... Generator Loss: 0.8127
Epoch 1/1... Discriminator Loss: 1.2746... Generator Loss: 0.8115
Epoch 1/1... Discriminator Loss: 1.3156... Generator Loss: 0.7927
Epoch 1/1... Discriminator Loss: 1.2837... Generator Loss: 0.8178
Epoch 1/1... Discriminator Loss: 1.2868... Generator Loss: 0.8087
Epoch 1/1... Discriminator Loss: 1.3443... Generator Loss: 0.7862
Epoch 1/1... Discriminator Loss: 1.3064... Generator Loss: 0.7057
Epoch 1/1... Discriminator Loss: 1.3806... Generator Loss: 0.8085
Epoch 1/1... Discriminator Loss: 1.2938... Generator Loss: 0.7661
Epoch 1/1... Discriminator Loss: 1.2616... Generator Loss: 0.7894
Epoch 1/1... Discriminator Loss: 1.3290... Generator Loss: 0.7850
Epoch 1/1... Discriminator Loss: 1.2261... Generator Loss: 0.7536
Epoch 1/1... Discriminator Loss: 1.2989... Generator Loss: 0.8160
Epoch 1/1... Discriminator Loss: 1.2766... Generator Loss: 0.7790
Epoch 1/1... Discriminator Loss: 1.2612... Generator Loss: 0.8334
Epoch 1/1... Discriminator Loss: 1.3055... Generator Loss: 0.7082
Epoch 1/1... Discriminator Loss: 1.3143... Generator Loss: 0.9483
Epoch 1/1... Discriminator Loss: 1.3036... Generator Loss: 0.7253
Epoch 1/1... Discriminator Loss: 1.2626... Generator Loss: 0.8470
Epoch 1/1... Discriminator Loss: 1.3679... Generator Loss: 0.6500
Epoch 1/1... Discriminator Loss: 1.2897... Generator Loss: 0.7415
Epoch 1/1... Discriminator Loss: 1.2744... Generator Loss: 0.6951
Epoch 1/1... Discriminator Loss: 1.3712... Generator Loss: 0.7408
Epoch 1/1... Discriminator Loss: 1.4458... Generator Loss: 0.7928
Epoch 1/1... Discriminator Loss: 1.2885... Generator Loss: 0.7484
Epoch 1/1... Discriminator Loss: 1.4145... Generator Loss: 0.7484
Epoch 1/1... Discriminator Loss: 1.1489... Generator Loss: 0.8270
Epoch 1/1... Discriminator Loss: 1.2607... Generator Loss: 0.7297
Epoch 1/1... Discriminator Loss: 1.2573... Generator Loss: 0.7591
Epoch 1/1... Discriminator Loss: 1.2354... Generator Loss: 0.7829
Epoch 1/1... Discriminator Loss: 1.2304... Generator Loss: 0.7355
Epoch 1/1... Discriminator Loss: 1.3377... Generator Loss: 0.7361
Epoch 1/1... Discriminator Loss: 1.1868... Generator Loss: 0.7992
Epoch 1/1... Discriminator Loss: 1.1200... Generator Loss: 0.9387
Epoch 1/1... Discriminator Loss: 1.2863... Generator Loss: 0.7633
Epoch 1/1... Discriminator Loss: 1.3651... Generator Loss: 0.7151
Epoch 1/1... Discriminator Loss: 1.2674... Generator Loss: 0.7484
Epoch 1/1... Discriminator Loss: 1.2906... Generator Loss: 0.7946
Epoch 1/1... Discriminator Loss: 1.2755... Generator Loss: 0.7736
Epoch 1/1... Discriminator Loss: 1.4079... Generator Loss: 0.7398
Epoch 1/1... Discriminator Loss: 1.2478... Generator Loss: 0.8186
Epoch 1/1... Discriminator Loss: 1.3612... Generator Loss: 0.6516
Epoch 1/1... Discriminator Loss: 1.3772... Generator Loss: 0.7893
Epoch 1/1... Discriminator Loss: 1.2700... Generator Loss: 0.7555
Epoch 1/1... Discriminator Loss: 1.0961... Generator Loss: 0.8657
Epoch 1/1... Discriminator Loss: 1.3389... Generator Loss: 0.7732
Epoch 1/1... Discriminator Loss: 1.4657... Generator Loss: 0.7115
Epoch 1/1... Discriminator Loss: 1.2421... Generator Loss: 0.7643
Epoch 1/1... Discriminator Loss: 1.2194... Generator Loss: 0.7864
Epoch 1/1... Discriminator Loss: 1.2549... Generator Loss: 0.7923
Epoch 1/1... Discriminator Loss: 1.2171... Generator Loss: 0.8412
Epoch 1/1... Discriminator Loss: 1.2255... Generator Loss: 0.7945
Epoch 1/1... Discriminator Loss: 1.1527... Generator Loss: 0.9011
Epoch 1/1... Discriminator Loss: 1.3103... Generator Loss: 0.7783
Epoch 1/1... Discriminator Loss: 1.2198... Generator Loss: 0.8607
Epoch 1/1... Discriminator Loss: 1.2491... Generator Loss: 0.7622
Epoch 1/1... Discriminator Loss: 1.1969... Generator Loss: 0.7379
Epoch 1/1... Discriminator Loss: 1.2115... Generator Loss: 0.8530
Epoch 1/1... Discriminator Loss: 1.0964... Generator Loss: 0.9198
Epoch 1/1... Discriminator Loss: 1.3058... Generator Loss: 0.8066
Epoch 1/1... Discriminator Loss: 1.2238... Generator Loss: 0.7103
Epoch 1/1... Discriminator Loss: 1.1784... Generator Loss: 0.8239
Epoch 1/1... Discriminator Loss: 1.2821... Generator Loss: 0.8378
Epoch 1/1... Discriminator Loss: 1.2468... Generator Loss: 0.8347
Epoch 1/1... Discriminator Loss: 1.3934... Generator Loss: 0.6877
Epoch 1/1... Discriminator Loss: 1.2457... Generator Loss: 0.6724
Epoch 1/1... Discriminator Loss: 1.2707... Generator Loss: 0.8122
Epoch 1/1... Discriminator Loss: 1.1757... Generator Loss: 0.9014
Epoch 1/1... Discriminator Loss: 1.2263... Generator Loss: 0.7554
Epoch 1/1... Discriminator Loss: 1.1004... Generator Loss: 0.9063
Epoch 1/1... Discriminator Loss: 1.2600... Generator Loss: 0.8456
Epoch 1/1... Discriminator Loss: 1.1536... Generator Loss: 0.9263
Epoch 1/1... Discriminator Loss: 1.2620... Generator Loss: 0.8077
Epoch 1/1... Discriminator Loss: 1.2037... Generator Loss: 0.8065
Epoch 1/1... Discriminator Loss: 1.2084... Generator Loss: 0.7982
Epoch 1/1... Discriminator Loss: 1.1548... Generator Loss: 0.8537
Epoch 1/1... Discriminator Loss: 1.2690... Generator Loss: 0.7421
Epoch 1/1... Discriminator Loss: 1.2598... Generator Loss: 0.7728
Epoch 1/1... Discriminator Loss: 1.1934... Generator Loss: 0.9276
Epoch 1/1... Discriminator Loss: 1.1307... Generator Loss: 0.7744
Epoch 1/1... Discriminator Loss: 1.3526... Generator Loss: 0.6613
Epoch 1/1... Discriminator Loss: 1.2905... Generator Loss: 0.8500
Epoch 1/1... Discriminator Loss: 1.1544... Generator Loss: 0.8512
Epoch 1/1... Discriminator Loss: 1.0860... Generator Loss: 0.9567
Epoch 1/1... Discriminator Loss: 1.1907... Generator Loss: 0.8086
Epoch 1/1... Discriminator Loss: 1.0920... Generator Loss: 0.9581
Epoch 1/1... Discriminator Loss: 1.2592... Generator Loss: 0.8983
Epoch 1/1... Discriminator Loss: 1.2063... Generator Loss: 0.7363
Epoch 1/1... Discriminator Loss: 1.2447... Generator Loss: 0.7296
Epoch 1/1... Discriminator Loss: 1.2932... Generator Loss: 0.7447
Epoch 1/1... Discriminator Loss: 1.1737... Generator Loss: 0.7768
Epoch 1/1... Discriminator Loss: 1.0321... Generator Loss: 1.0489
Epoch 1/1... Discriminator Loss: 1.2124... Generator Loss: 0.9498
Epoch 1/1... Discriminator Loss: 1.3006... Generator Loss: 0.8591
Epoch 1/1... Discriminator Loss: 1.2120... Generator Loss: 0.8207
Epoch 1/1... Discriminator Loss: 1.1502... Generator Loss: 0.7947
Epoch 1/1... Discriminator Loss: 1.2717... Generator Loss: 0.7557
Epoch 1/1... Discriminator Loss: 1.2634... Generator Loss: 0.6903
Epoch 1/1... Discriminator Loss: 1.2937... Generator Loss: 0.6991
Epoch 1/1... Discriminator Loss: 1.1126... Generator Loss: 1.0248
Epoch 1/1... Discriminator Loss: 1.2897... Generator Loss: 0.7650
Epoch 1/1... Discriminator Loss: 1.3532... Generator Loss: 0.8220
Epoch 1/1... Discriminator Loss: 1.2140... Generator Loss: 0.8379
Epoch 1/1... Discriminator Loss: 1.1999... Generator Loss: 0.8092
Epoch 1/1... Discriminator Loss: 1.2880... Generator Loss: 0.7936

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.